Software & Shinyapps

Website projects:

• www.digitalpolitics.info: A pipeline that scrapes politicians’ twitter data and produces a real-time report analyzing their activity and the content of their tweets. The project feeds the Digital Politics Twitter Bot that tweets statistics on a daily basis.

R packages:

• deeplr: This R-package provides functionality for quick translations using the DeepL Translator from within R (install the package from Github). You will also find examples on Github. DeepL now has an official API and you need to apply for access, i.e. to get an API key https://www.deepl.com/api-contact.html.
• citationsr: The R package citationsr comprises functions that can be used to extract and analyze citation cases. When study A cites study B, it contains text fragments that refer to study B. We call study A a citing document and the text fragments it contains citation cases. Go to the github repository for an overview.

Shinyapps:

Throughout the last years I have developed various shiny apps for teaching and research. I lot of the stuff is open source and can be found on my Github page.

• Methods & statistics
  • Measurement error & Bull’s eye (2015): Illustration of systematic and random measurement error. Example is a single person that repeatedly measures his/her weight on a scale. Students can change the number of measurements (observations), i.e. how often the person measures his/her weight, as well as the random error and the systematic error underlying these repeated measurements. You can find the app here and the code on github.
  • Visualizing Causal Scenarios [interactively]: A shiny app under development to visualize causal scenarios. You can find the corresponding paper here. The graph shown above is related to that project and the code shall be collected in an R package.
  • Guessing empirical distributions: Let your students guess what distributions of various variables look like and discuss them subsequently. You can find the code on github.
  • Visualizing functions: Plot functions. User can choose a certain function, decide about the range for which the function should be plotted and the range of x- and y-values for which the plot is displayed. You can find the code on github.
  • Transformations of variables/data: A simple app to illustrate what happens to the the distribution of a variable when it is transformed. You can find the code on github.
  • Joint distributions (discrete variables): Again the idea is that students become familiar with the idea of joint distributions, i.e. develop a “distributional perspective” of data. You can find the code on github.
  • Systematic measurement error in subgroups: Illustration of how distributions of variables change as a consequence of measurement error. It also illustrates how distributions change if there are different systematic measurement errors across subgroups that operate simultaneously. You can find the code on github.
    
    
• Data visualization
  • Styling markers (Plotly R): A shiny app to illustrate different marker settings for Plotly in R, e.g., to better visualize density.
  • Styling layout (Plotly R): A shiny app to facilitate understanding & testing of different layout choices with R plotly, e.g., changing margins etc.
  • Styling 3d layout & camera perspective (Plotly R): An app to help understanding & realizing different R plotly 3d layouts and camera perspectives.